Most businesses today have a little issue collecting data; the challenging—and time-consuming—part is deciding what to do with it. Even excellent firms struggle to develop wise business judgments following careful data analysis. A decision support system (DSS) is beneficial in bridging the gap between data analytics and choices.

A DSS may assist businesses in navigating their data environment and feeling confident in their judgments when decision-making methods aren’t functioning. Data is collected, analyzed, and included in thorough reports using a DSS. Artificial intelligence (AI), people, decision-makers, or a combination may control the decision-support system technique entirely.

Businesses may employ augmented analytics, or artificial intelligence-based decision-making approaches, as a potent tool to leverage data to make wise business choices confidently.

Decision support systems come in various shapes and sizes, from a robo-advisor guiding young investors in the stock market to an internet streaming service suggesting TV shows.

Below are a few examples of real-world applications for AI-powered DSS called intelligent decision support systems (IDSS).


One excellent example of decision support systems in financial technology is robo-advisors. Besides an initial self-assessment procedure to choose investment possibilities, financial robo-advisors use economic models to manage online investment portfolios with little human input or engineering.

This kind of DSS outsources investment suggestions to appeal to the customer and employs AI to provide guidance based on previous results.


To aid in identifying cancer, radiologists utilize clinical decision support systems through AI-powered image processing software. Health informatics management, including the upkeep and assessment of research data about particular protocols, preventive treatment, and disease diagnosis, may also be done using DSS.

DSS may assist healthcare organizations in analyzing patient data to enhance company performance, patient outcomes, and healthcare costs.


When marketers utilize AI-powered decision support systems to construct buyer personas by examining how customers engage with various brand features or across many brands, this is another example of a knowledge-based intelligent assistance system.


DSS is used for eCommerce sites to provide customer suggestions based on factors like past purchases or browsing history. Using a decision support system, companies may provide customized recommendations based on customer information and consumption habits. For instance, many eCommerce sites will utilize the things you’ve previously seen or suggested and those other users have purchased. Using DSS for supply chain inventory forecasting may help companies keep ahead of production demand and optimize shipping for better customer satisfaction and brand reputation.


One widely utilized decision assistance system in use today is GPS. A GPS will analyze all of the alternatives and assist in planning the quickest path between two places. Most GPS systems also have real-time traffic monitoring, which aids drivers in avoiding gridlock.

Actual estate

DSS is also very useful in the real estate industry: Businesses employ decision support technologies to collect information on land, general business growth, neighbourhood pricing comparisons, and future planning.


Even farmers utilize DSS technologies to plan when to sow, fertilize, and harvest their crops according to the environment.

Artificial intelligence and decision support systems

Effective decision support systems are built on artificial intelligence. A decision support system facilitates data-based decision-making for a group or organization. Expert systems, also known as AI capabilities, automate business decision-making.

A DSS will use business information and speed up decision-making by predicting outcomes using much data. According to a list by University of Northern Iowa professor Daniel Power, many kinds of decision support systems benefit businesses depending on the information source.

Data-driven DSS is the most common type of DSS, and all management reporting systems fall into it.

Data-driven based on big datasets of both internal and external data, DSS will provide suggestions.

Model-driven: This less data-intensive method might include representational, optimization, or accounting/financial models.

Document-driven: These decision-support tools aid in document retrieval and analysis. Among these are search engines.

Knowledge-driven: These DSS advise managers to take specific actions and provide problem-solving based on a particular problem or subject. For instance, a knowledge-driven DSS tool might assist management in improved planning, result prediction, or uncertainty reduction.

Communication-driven: DSS aims to make teamwork and communication more efficient.

AI tries to work like the human brain by using artificial neural networks, which are a group of algorithms that find connections and patterns in data. Then, AI systems may develop optimization techniques to aid firms in making wise choices.

AI excels as a decision-support tool because of its predictive capabilities, which can transform unstructured data into practical guidance.


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